joseph.ergo@proton.me | Portfolio | Resume PDF | Linked-In | +212 713-617-633

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PROMETRIC

Table of contents

• 1 About the project
• 2 EDA
  • 2.1 information technology and services indestry’s yearly new recruit count
  • 2.2 prometric’s workforce status over the years
• 3 Persona company network graph
• 4 Workforce sample
  • 4.1 Arjan De Wit
  • 4.2 Art Healey
  • 4.3 Beatrice Rutayisire
  • 4.4 Brad Cpfm
  • 4.5 Brent Stineman
  • 4.6 Brooke Smith
  • 4.7 Carla Griffith
  • 4.8 Cengiz Ozkaynak
  • 4.9 Cf-Sp Brad Peters; Cfbe
  • 4.10 Chad Dyar
  • 4.11 Christine Hayek
  • 4.12 D.B.A Charice Hayes
  • 4.13 Dallas Coffman
  • 4.14 Ed Mooers
  • 4.15 Farvardin Jahansooz
  • 4.16 Felicia Boamah
  • 4.17 Hanneke Van Der Helm
  • 4.18 Janine Allen
  • 4.19 Kiana Dancil
  • 4.20 Lyn Parker
  • 4.21 Marc Solis
  • 4.22 Martin Peen
  • 4.23 Michael Irwin
  • 4.24 Michael Re
  • 4.25 Mike Calvo
  • 4.26 Patricia Fullagar
  • 4.27 Patrick Maher
  • 4.28 S Christine Cavataio
  • 4.29 Sharon Fleming
  • 4.30 Stephen Jones

## SETUP
from pathlib import Path
import duckdb
from tqdm.notebook import tqdm
import datetime
import copy
import polars as pl
import plotly.express as px
import plotly.io as pio
import re
from concurrent.futures import ThreadPoolExecutor
import plotly.graph_objects as go
import networkx as nx
import numpy as np
# pio.renderers.default = 'plotly_mimetype'
pio.renderers.default = 'jupyterlab+notebook'
pio.templates.default = "plotly_white"

path_data = Path.cwd()/'data'/'03_rdb'
path_data_companies = path_data/'companies_table.parquet'
path_data_experience = path_data/'experience_table.parquet'
path_data_emails = path_data/'emails_table.parquet'
path_data_education = path_data/'education_table.parquet'
path_data_school = path_data/'school_table.parquet'
path_data_persona = path_data/'persona_table.parquet'
path_data_profiles = path_data/'profiles_table.parquet'

path_output_images = Path.cwd()/'output'/'images'

conn = duckdb.connect()

conn.execute("SET temp_directory = 'temp';")
conn.execute("SET memory_limit = '10GB';")
conn.execute("SET max_temp_directory_size = '100GB';")
conn.execute("SET threads = 8;")
conn.execute("SET preserve_insertion_order = false;")
conn.execute("SET enable_progress_bar = true;")
conn.execute("SET enable_progress_bar_print = true;")

df = pl.read_parquet('03_target_companies3.parquet')
df_yearly_new_hires_per_indestry = pl.read_parquet('03_yearly_new_hires_per_indestry.parquet')

current_company_id = "&-friends"
current_company_id = pl.read_json("04__control__.json")[0,'current_company_id']
query = f"""
SELECT *
FROM read_parquet('{path_data_companies}')
WHERE company_id = '{current_company_id}'
"""
df_company_by_company_id = pl.DataFrame(conn.execute(query).df())

current_company_name = df_company_by_company_id[0,'company_name']
current_company_indestry = df_company_by_company_id[0,'company_industry']

current_company_parquet = Path.cwd()/'output'/'company_data'/f"{current_company_id}.parquet"

# Info about personas status from company_id

query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE company_id = '{current_company_id}'
"""
df_experiences_by_company_id = pl.DataFrame(conn.execute(query).df())

personas_whitout_end_date = df_experiences_by_company_id.filter(pl.col('end_date').is_null())
personas_who_got_raise = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) &
                                     pl.col('persona_id').is_in(personas_whitout_end_date['persona_id'].to_list()))
personas_who_stayed = (pl
                      .concat([personas_whitout_end_date, personas_who_got_raise])
                      .sort('start_date')
                      .group_by('persona_id')
                      .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                      )
                      .with_columns(
                          pl.lit(True).alias('still_associated'),
                          pl.lit(None).alias('end_date')
                      )
                      .sort('changes')
                             )

personas_who_left = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) & ~pl.col('persona_id').is_in(personas_who_stayed['persona_id'].to_list()) )
personas_who_left = (personas_who_left
                     .sort('start_date')
                     .group_by('persona_id')
                     .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                              )
                     .with_columns(
                         pl.lit(False).alias('still_associated'),
                         
                     )
                     .sort('changes'))

df_personas_who_worked_in_company = pl.concat([personas_who_stayed, personas_who_left], how='vertical_relaxed').with_columns(
    (pl.col('end_date').dt.year()-pl.col('start_date').dt.year()).alias('work_durration')
).sort('work_durration')

import dns.resolver
import smtplib
import socket

def check_deliverability(email_address):
    """
    Checks the deliverability of an email address by verifying MX records
    and performing an SMTP connection test.
    """
    if '@' not in email_address:
        return False
    
    domain = email_address.split('@')[1]
    
    # Check for MX records
    try:
        mx_records = dns.resolver.resolve(domain, 'MX')
        if not mx_records:
            return False
    except (dns.resolver.NoAnswer, dns.resolver.NXDOMAIN, dns.resolver.Timeout):
        return False

    # Perform SMTP connection test
    mx_host = str(mx_records[0].exchange)
    
    # Validate MX hostname before attempting connection
    try:
        # Test if hostname can be properly encoded
        mx_host.encode('idna')
    except UnicodeError:
        return False
    
    try:
        with smtplib.SMTP(mx_host, timeout=10) as smtp:
            smtp.set_debuglevel(0)
            smtp.helo(socket.gethostname())
            smtp.mail('test@example.com')
            code, _ = smtp.rcpt(email_address)

            return code == 250  # 250 indicates valid email address
            
    except (smtplib.SMTPException, socket.error, UnicodeError):
        return False

# info of all personas info
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_persona}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas = pl.DataFrame(conn.execute(query).df())

# info of all personas profiles
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_profiles}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_profile = pl.DataFrame(conn.execute(query).df())
df_all_personas_profile_f = df_all_personas_profile.group_by('persona_id').agg(pl.col('url').unique())

# info of all personas email
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_emails}')
WHERE persona_id IN ({list_for_in}) AND type == 'personal'
"""
df_all_personas_emails = pl.DataFrame(conn.execute(query).df())

def def_polars_fix_gmail(x):
    if "@gmail" in x:
        first_part = x.split('@')[0]
        second_part = x.split('@')[1]
        return f"{first_part.replace(".",'')}@{second_part}"
    else:
        return x

df_all_personas_emails_f = (df_all_personas_emails
                            .with_columns(pl.col('address')
                                          .map_elements(def_polars_fix_gmail, return_dtype=pl.String)
                                          .alias('normalised_emails'))
                            .unique('normalised_emails', keep='first')
                            .sort('persona_id')
                            .drop('normalised_emails')
                         )
df_all_personas_emails_f = (df_all_personas_emails_f.group_by('persona_id').agg(pl.col('address').unique(),pl.col('type').unique()))
df_all_personas_plus = df_all_personas.join(df_all_personas_emails_f, on='persona_id', how='left')

df_full_personas_who_worked_in_company = (df_personas_who_worked_in_company
                                       .join(df_all_personas_plus, on='persona_id', how='left')
                                       .join(df_all_personas_profile_f, on='persona_id', how='left')
                                      )

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        (pl.col("start_date").fill_null(pl.col("start_date").min()))
        .dt.year()
        .alias("start_year"),
        (pl.col("end_date").dt.year()).alias("end_year"),
    )
)

work_years = []
for i in range(len(df_full_personas_who_worked_in_company)):
    start_y = df_full_personas_who_worked_in_company[i, "start_year"]
    if df_full_personas_who_worked_in_company[i, "end_year"]:
        end_y = df_full_personas_who_worked_in_company[i, "end_year"]
    else:
        end_y = 2020

    tmp_work_years = []
    for y in range(start_y, end_y + 1):
        tmp_work_years.append(y)

    work_years.append(tmp_work_years)

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        pl.Series("work_years", work_years)
    )
)

# add hireups
title_name_match = ["ceo","chief","founder","owner","president","vp","vice","director",
    "cfo","cto","partner","head of","hr ","human","talent","senior","manager","lead"]

df_full_personas_who_worked_in_company = (df_full_personas_who_worked_in_company
    .with_columns(
        pl.when(pl.col('title_name').str.contains_any(title_name_match)).then(True).otherwise(False).alias("higher_up")
    ))



df_tmp_email_checker = (
    df_full_personas_who_worked_in_company
    .filter(
            pl.col('still_associated')==True,
            pl.col('address').list.len()>0
    )
        ['persona_id','address']
        .explode('address')
)

# if current_company_parquet.exists():
#     df_pre_full_personas_who_worked_in_company = pl.read_parquet(current_company_parquet)
#     list_pre_deliverable_address = df_pre_full_personas_who_worked_in_company['address'].drop_nulls().explode().to_list()
# else:
#     list_pre_deliverable_address = []

# list_of_emails_to_check = df_tmp_email_checker['address'].drop_nulls().to_list()
# list_lists_email_check = []

# var_total_emails = len(list_of_emails_to_check)
# var_current_email_count = 0

# def def_check_and_populate(email_to_check):
#     global list_lists_email_check, var_current_email_count
#     if email_to_check in list_pre_deliverable_address:
#         list_lists_email_check.append([email_to_check, True])
#     elif '@gmail' in email_to_check:
#         list_lists_email_check.append([email_to_check, True])
#     else:
#         try:
#             is_deliverable = check_deliverability(email_to_check)
#             list_lists_email_check.append([email_to_check, is_deliverable])
#         except:
#             list_lists_email_check.append([email_to_check, False])
#     var_current_email_count += 1
#     print(' '*10, end='\r')
#     print(round(var_current_email_count/var_total_emails,5), end='\r')

# with ThreadPoolExecutor(max_workers=20) as executor:
#     results = list(executor.map(def_check_and_populate, list_of_emails_to_check))

# df_email_check = pl.DataFrame(list_lists_email_check, schema=["address", "deliverable"], orient="row")
# try:
#     df_tmp_email_checker_f = (
#         df_tmp_email_checker
#             .join(df_email_check, on='address')
#             .filter(pl.col('deliverable')==True)
#             .group_by('persona_id').agg(pl.col('address').unique().alias("deliverable_address"))
#     )
# except:
#     df_tmp_email_checker_f = pl.DataFrame()

# if df_tmp_email_checker_f.is_empty():
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker.rename({'address':'deliverable_address'}), on="persona_id", how='left')
# else:
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker_f, on="persona_id", how='left')

# Info about personas experiences

# info of all experiences[]
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_experiences = pl.DataFrame(conn.execute(query).df())


# info of all comapnies in said experiences
list_w = []
for word in df_all_personas_experiences['company_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT company_id, company_name, company_industry, company_linkedin_url, company_location_country
FROM read_parquet('{path_data_companies}')
WHERE company_id IN ({list_for_in})
"""
df_all_companies = pl.DataFrame(conn.execute(query).df())

df_full_personas_experiences_plus = df_all_personas_experiences.join(df_all_companies, on='company_id', how='left')

df_full_personas_experiences_plus = (
    df_full_personas_experiences_plus
    .with_columns(
        pl.when(
            pl.col('company_id')==current_company_id
        )
        .then(True)
        .otherwise(False)
        .alias('target')
    )
)

# Info about personas education

# info of all experiences
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_education}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_education = pl.DataFrame(conn.execute(query).df())


#ifon of allcomapnies in said experiences
list_w = []
for word in df_all_personas_education['school_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

if list_w:
    list_for_in = ', '.join(list_w)
    query = f"""
    SELECT school_id, school_name, school_type, school_website, school_location_country
    FROM read_parquet('{path_data_school}')
    WHERE school_id IN ({list_for_in})
    """
    df_all_school = pl.DataFrame(conn.execute(query).df())
    
    df_full_personas_education_plus = df_all_personas_education.join(df_all_school, on='school_id', how='left')
else:
    df_full_personas_education_plus = df_all_personas_education

1 About the project

The project came to life after realizing that web scraping doesn’t allow deep-level filtering—without consuming too much time.The irony is, this project itself took me about a month, but the final RDB contains more data than I could ever scrape.

The raw data was 1.4 TB in size and holds information previously scraped.
Processing was done on my local machine using Python, Polars, and DuckDB, following this workflow:
- Processed raw data into structured Parquet files using Polars.
- Transformed each Parquet file into mini RDBs using Polars.
- Merged all mini RDBs into one using DuckDB.
- Analyzed and filtered data to fit the current project.

2 EDA

2.1 information technology and services indestry’s yearly new recruit count

list_of_unique_company_experience_years = []
for y in df_full_personas_who_worked_in_company['start_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)
for y in df_full_personas_who_worked_in_company['end_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)

list_year = []
list_state = []
list_count = []
list_names = []

def def_get_names_breked(tmp):
    if tmp.is_empty():
        names_string = ''
    else:
        tmp_list_name = []
        names_limit = 3
        row_limit = names_limit * 6
        for i, name in enumerate(tmp['full_name'].to_list()):
            ii = i+1
            tmp_list_name.append(name.title())
            if ii!=0 and ii%names_limit==0:
                tmp_list_name.append("<br>")
            if ii==row_limit:
                tmp_list_name.append("...")
                break
        names_string = ', '.join(tmp_list_name).replace(", <br>, ","<br>")
    return names_string

for y in list_of_unique_company_experience_years:
    #recuite state
    list_year.append(y)
    list_state.append('Recruited')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('start_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))
    
    #recuite state
    list_year.append(y)
    list_state.append('Resigned')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('end_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))

df_m_recruite_vs_resign = pl.DataFrame({
    'year':list_year,
    'status':list_state,
    'count':list_count,
    'names':list_names,})

2.2 prometric’s workforce status over the years

3 Persona company network graph

gr_net = df_full_personas_experiences_plus.with_columns(pl.col('company_id').str.to_uppercase()).group_by('persona_id','company_id').agg(pl.len().alias('count')).sort('count')
list_top_in_network = gr_net['company_id'].value_counts().sort('count', descending=True)['company_id'].to_list()[:5]
gr_net_f = gr_net.filter(pl.col('company_id').is_in(list_top_in_network))

list_letters = ['A','B','C','D','E','F','G','H']
dict_company = {}
dict_company_rev = {}
for company, letter in zip(list_top_in_network, list_letters ):
    dict_company[letter] = company
    dict_company_rev[company] = letter

gr_gr_net_f = gr_net_f.sort('company_id').group_by('persona_id').agg(pl.col('company_id').unique().sort(),)

gr_gr_net_f2 = (
    gr_gr_net_f['company_id']
    .value_counts()
    .with_columns(
        # pl.col('company_id').list.join(', '),
        (pl.col('count')/len(gr_gr_net_f)).alias('per')
    )
    .sort('per',descending=True)
)

list_prob = []
for i in range(len(gr_gr_net_f2)):
    tmp_prob_letters = []
    for k in dict_company.keys():
        if dict_company[k] in gr_gr_net_f2[i]['company_id'][0].to_list():
            tmp_prob_letters.append(f' {k}')
        else:
            tmp_prob_letters.append(f'¬{k}')

    list_prob.append(f"P({' ∩ '.join(tmp_prob_letters)}) = {round(gr_gr_net_f2[i]['per'][0],4)}")
annon_prob_text = "<b>Probability Distribution:</b><br>" + '<br>'.join(list_prob)



# Create network graph
G = nx.Graph()
for persona, company in gr_net_f.select(['persona_id', 'company_id']).iter_rows():
    G.add_edge(persona, company)

# Get unique values
persona_ids = gr_net_f['persona_id'].unique().to_list()
company_ids = gr_net_f['company_id'].unique().to_list()

# Calculate degrees (connection counts)
degree_dict = dict(G.degree())

# Get min and max degrees for scaling
company_degrees = [degree_dict[c] for c in company_ids]
persona_degrees = [degree_dict[p] for p in persona_ids]

min_company_degree = min(company_degrees) if company_degrees else 1
max_company_degree = max(company_degrees) if company_degrees else 1
min_persona_degree = min(persona_degrees) if persona_degrees else 1
max_persona_degree = max(persona_degrees) if persona_degrees else 1

# Define size ranges
COMPANY_MIN_SIZE = 25
COMPANY_MAX_SIZE = 100
PERSONA_MIN_SIZE = 5
PERSONA_MAX_SIZE = 20

# print(f"Company connections range: {min_company_degree} - {max_company_degree}")
# print(f"Persona connections range: {min_persona_degree} - {max_persona_degree}")

# Sort companies by degree (size) in descending order
company_ids_sorted = sorted(company_ids, key=lambda x: degree_dict[x], reverse=True)

# Check if "Nokia" exists in the data
HIGHLIGHTED_COMPANY = current_company_id
HIGHLIGHTED_COMPANY_EXISTS = HIGHLIGHTED_COMPANY.lower() in [str(c).lower() for c in company_ids]

if HIGHLIGHTED_COMPANY_EXISTS:
    # Get the actual case-sensitive name
    highlighted_company = next(c for c in company_ids if str(c).lower() == HIGHLIGHTED_COMPANY.lower())
    # print(f"Highlighting company: {highlighted_company} (with {degree_dict[highlighted_company]} connections)")
else:
    # print(f"Warning: '{HIGHLIGHTED_COMPANY}' not found in company list")
    highlighted_company = None

# Create layout (companies on outer circle, ordered by size)
pos = {}
num_companies = len(company_ids_sorted)
radius_outer = 2.0

# Position companies on circle, ordered by size (largest first)
for i, company in enumerate(company_ids_sorted):
    # Start at top (90° or π/2 radians) and go counter-clockwise (add angle)
    # Counter-clockwise rotation: angle = start_angle + (i * 2π / num_companies)
    # This puts largest at top, next on left, then bottom, then right
    start_angle = np.pi / 2  # 90° at top
    
    # For counter-clockwise rotation
    angle = start_angle - (2 * np.pi * i / num_companies)
    
    # Convert to x, y coordinates
    pos[company] = (radius_outer * np.cos(angle), radius_outer * np.sin(angle))

# Position personas
for i, persona in enumerate(persona_ids):
    connected_companies = [c for c in company_ids if G.has_edge(persona, c)]
    if connected_companies:
        avg_x = np.mean([pos[c][0] for c in connected_companies])
        avg_y = np.mean([pos[c][1] for c in connected_companies])
        # Add jitter to spread out personas
        jitter_x = np.random.uniform(-0.2, 0.2)
        jitter_y = np.random.uniform(-0.2, 0.2)
        pos[persona] = (avg_x * 0.5 + jitter_x, avg_y * 0.5 + jitter_y)
    else:
        pos[persona] = (0, 0)

# Prepare edge traces
edge_x, edge_y = [], []
for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_x.extend([x0, x1, None])
    edge_y.extend([y0, y1, None])

edge_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.6, color='rgba(120, 120, 120, 0.15)'),
    hoverinfo='none',
    mode='lines')

# Prepare node traces with proportional sizing
company_x, company_y, company_text = [], [], []
company_color, company_size, company_hover = [], [], []
company_border_width = []  # For border thickness
company_border_color = []  # For border color

persona_x, persona_y = [], []
persona_color, persona_size, persona_hover = [], [], []

# Helper function to scale size proportionally
def scale_size(value, min_val, max_val, min_size, max_size):
    if max_val == min_val:
        return (min_size + max_size) / 2
    return min_size + (value - min_val) / (max_val - min_val) * (max_size - min_size)

# Add COMPANY nodes in sorted order (largest first)
for company in company_ids_sorted:
    x, y = pos[company]
    company_x.append(x)
    company_y.append(y)
    company_text.append(str(company))
    company_color.append('#EF553B')
    
    connections = degree_dict[company]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections, 
        min_company_degree, 
        max_company_degree,
        COMPANY_MIN_SIZE, 
        COMPANY_MAX_SIZE
    )
    company_size.append(scaled_size)
    
    # Custom border for highlighted company
    if highlighted_company and company == highlighted_company:
        company_border_width.append(4)  # Thicker border
        company_border_color.append('#000000')  # Black border
    else:
        company_border_width.append(1)
        company_border_color.append('#000000')
    
    # Hover text
    personas = gr_net_f.filter(pl.col('company_id') == company)['persona_id'].to_list()
    rank = company_ids_sorted.index(company) + 1
    hover_text = f"<b>Company #{rank}:</b> {company}<br>"
    hover_text += f"<b>Personas worked here:</b> {connections}<br>"
    hover_text += f"<b>Connection rank:</b> {rank}/{len(company_ids_sorted)}<br>"
    if connections > 0:
        for persona in personas[:5]:
            persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
            hover_text += f" • {persona_name}<br>"
        if connections > 5:
            hover_text += f" • ... and {connections - 5} more"
    company_hover.append(hover_text)

# Add PERSONA nodes
for persona in persona_ids:
    x, y = pos[persona]
    persona_x.append(x)
    persona_y.append(y)
    persona_color.append('#636efa')
    
    connections = degree_dict[persona]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections,
        min_persona_degree,
        max_persona_degree,
        PERSONA_MIN_SIZE,
        PERSONA_MAX_SIZE
    )
    persona_size.append(scaled_size)
    
    # Hover text
    companies = gr_net_f.filter(pl.col('persona_id') == persona)['company_id'].to_list()
    persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
    hover_text = f"<b>Persona:</b> {persona_name}<br>"
    hover_text += f"<b>Companies worked at:</b> {connections}<br>"
    if connections > 0:
        # Check if worked at highlighted company
        if highlighted_company:
            worked_at_highlighted = highlighted_company in companies
            if worked_at_highlighted:
                hover_text += f"<b>Worked at {highlighted_company}:</b> ✓<br>"
        
        hover_text += "<br>".join([f"  • {comp}" for comp in companies[:5]])
        if connections > 5:
            hover_text += f"<br>  • ... and {connections - 5} more"
    persona_hover.append(hover_text)

# Create company node trace
company_trace = go.Scatter(
    x=company_x, y=company_y,
    mode='markers+text',
    hoverinfo='text',
    hovertext=company_hover,
    text=company_text,
    textposition="top center",
    textfont=dict(size=14, color='black'),
    marker=dict(
        color=company_color,
        size=company_size,
        line=dict(
            width=company_border_width,
            color=company_border_color
        ),
        opacity=0.9)
)

# Create persona node trace
persona_trace = go.Scatter(
    x=persona_x, y=persona_y,
    mode='markers',
    hoverinfo='text',
    hovertext=persona_hover,
    text=None,  # No text for personas
    marker=dict(
        color=persona_color,
        size=persona_size,
        line=dict(width=1, color='black'),
        opacity=0.7)
)

# Calculate axis ranges for 1:1 aspect ratio
all_positions = list(pos.values())
x_vals = [p[0] for p in all_positions]
y_vals = [p[1] for p in all_positions]

# Add padding
x_range = [min(x_vals) - 0.5, max(x_vals) + 0.5]
y_range = [min(y_vals) - 0.5, max(y_vals) + 0.5]

# Make axes have the same range for 1:1 aspect
max_range = max(x_range[1] - x_range[0], y_range[1] - y_range[0])
x_center = (x_range[0] + x_range[1]) / 2
y_center = (y_range[0] + y_range[1]) / 2

x_range = [x_center - max_range/2, x_center + max_range/2]
y_range = [y_center - max_range/2, y_center + max_range/2]

# Create figure with 1:1 aspect ratio
fig = go.Figure(data=[edge_trace, persona_trace, company_trace],
                layout=go.Layout(
                    title=f'Persona-Company Network (Companies Ordered by Size)<br><sup>Highlighted: {highlighted_company if highlighted_company else "None"}</sup>',
                    showlegend=False,
                    hovermode='closest',
                    margin=dict(b=20, l=20, r=20, t=100),
                    xaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=x_range,
                        scaleanchor="y",
                        scaleratio=1
                    ),
                    yaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=y_range
                    ),
                    plot_bgcolor='white',
                    paper_bgcolor='white',
                    width=900,
                    height=900
                ))

# Add legend with size examples and highlighting info
# legend_text = f"""
# <b>Node Size = Connection Count</b><br>
# <span style='color:#EF553B'>● Companies</span><br>
# <span style='color:#636efa'>● Personas</span> (hover for details)
# """

# fig.add_annotation(
#     x=0.98, y=0.98,
#     xref="paper", yref="paper",
#     text=legend_text,
#     showarrow=False,
#     font=dict(size=14),
#     align="left",
#     bgcolor="rgba(255, 255, 255, 0.95)",
    
# )

# Add top companies list
top_companies = company_ids_sorted[:10]  # Top 10 companies
top_companies_text = "<b>Top Companies by Connections:</b><br>"
for i, company in enumerate(top_companies, 1):
    connections = degree_dict[company]
    top_connections = degree_dict[top_companies[0]]
    connections_per = f" | {round(connections/top_connections*100)}%" if highlighted_company and company != highlighted_company else ""
    highlight_indicator = " " if highlighted_company and company == highlighted_company else ""
    top_companies_text += f"{dict_company_rev[company]}. {company}: {connections} {connections_per} {highlight_indicator}<br>"

fig.add_annotation(
    x=0.02, y=0.98,
    xref="paper", yref="paper",
    text=top_companies_text,
    showarrow=False,
    font=dict(size=14),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.9)",
    # bordercolor="#666",
    # borderwidth=1
)

# Add probabiliy list

fig.add_annotation(
    x=0.98, y=0.98,
    xref="paper", yref="paper",
    text=annon_prob_text,
    showarrow=False,
    font=dict(
        family="'Courier New', monospace",  # Multiple fallbacks
        size=12,
        color="black"
    ),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.95)",
    
)
fig.write_image((path_output_images/f'network_{current_company_id}.webp'))
fig.show()

amount = 5

tmp = df_full_personas_who_worked_in_company.sort(
    ["inferred_salary", "linkedin_connections", "inferred_years_experience"],
    descending=True,
)
tmp_gr = df_full_personas_experiences_plus.group_by('persona_id').agg(pl.len().alias('experience_count'))
tmp = df_full_personas_who_worked_in_company.join(tmp_gr, on='persona_id').sort('experience_count',descending=True)

tmp2 = pl.concat(
    [tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==True)[:amount*2],
     tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==False)[:amount*2],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==True)[:amount*1],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==False)[:amount*1],
    ]
).sort("full_name")

list_persona_for_plot = tmp2['persona_id'].to_list()

# Workforce data

def def_plotly_experience_range(current_persona_id):
    tmp_df = (df_full_personas_who_worked_in_company
              .filter(pl.col('persona_id')==current_persona_id)
              .with_columns(pl.col('end_year').fill_null(2021))['start_year','end_year'])
    
    fig_tmp = copy.deepcopy(fig_company_hiring_trend)
    fig_tmp.add_vrect(
        x0=tmp_df[0,'start_year'],
        x1=tmp_df[0,'end_year'],
        fillcolor="blue",
        opacity=0.1,
        line_width=0 
    )
    return fig_tmp

def def_plotly_experience_gantt(current_persona_id):
    px_data = (df_full_personas_experiences_plus
               .filter(pl.col('persona_id')==current_persona_id)
               .with_columns(
                   pl.col('end_date').fill_null(datetime.datetime(2020, 1, 1, 0,0)),
                   pl.col('company_name').str.to_uppercase(),
                   # pl.col('company_name').str.to_uppercase().str.replace_all('&', '-and-')
               )
               .sort('start_date'))
    
    y_order = px_data['company_name'].to_list()
    
    fig = px.timeline(px_data,x_start="start_date", x_end="end_date", y="company_name",
                      color='target',hover_data=["title_name"], height=140+30*len(px_data),
                      category_orders={"company_name": y_order},
                      color_discrete_map={True:'#EF553B',  False:'#636efa'},
                      labels={'target':'Target', 'start_date':'Recruited', 'end_date':'If-Resigned', 
                             'company_name':'Company', 'title_name':'Job role'}
                     # title=f"Experience of {current_persona_name}.",
                     )
    fig.update_yaxes(
        # autorange="reversed",
                              showgrid=True,
                              gridcolor='lightgray',
                              gridwidth=1,
                              griddash='dot'
    )
    fig.update_layout(showlegend=False, xaxis_title=None, yaxis_title=None)
    return fig

4 Workforce sample

4.1 Arjan De Wit

Job title: M.i.s representative
Socials: https://linkedin.com/in/arjan-de-wit-103a8b2 | https://twitter.com/arjandw | https://linkedin.com/in/dewit

4.1.1 Arjan De Wit’s working period at prometric

4.1.2 Gantt plot of Arjan De Wit’s experience

---

4.2 Art Healey

Job title: Finance officer
Socials: https://foursquare.com/user/63931822 | https://linkedin.com/in/arthealey | https://facebook.com/arthur.healey

4.2.1 Art Healey’s working period at prometric

4.2.2 Gantt plot of Art Healey’s experience

---

4.3 Beatrice Rutayisire

Job title: Test centre administrator
Socials: https://linkedin.com/in/beatricemrutayisire

4.3.1 Beatrice Rutayisire’s working period at prometric

4.3.2 Gantt plot of Beatrice Rutayisire’s experience

---

4.4 Brad Cpfm

Job title: Prometric approved proctor
Socials: https://linkedin.com/in/chefbradpeters | https://twitter.com/chefdbpeters

4.4.1 Brad Cpfm’s working period at prometric

4.4.2 Gantt plot of Brad Cpfm’s experience

---

4.5 Brent Stineman

Job title: Technical team lead and architect
Socials: https://linkedin.com/in/brent-stineman-2a95053 | https://twitter.com/brentcodemonkey | https://github.com/brentstineman | https://linkedin.com/in/bstineman

4.5.1 Brent Stineman’s working period at prometric

4.5.2 Gantt plot of Brent Stineman’s experience

---

4.6 Brooke Smith

Job title: Chief marketing officer
Socials: https://linkedin.com/in/brookesmithws

4.6.1 Brooke Smith’s working period at prometric

4.6.2 Gantt plot of Brooke Smith’s experience

---

4.7 Carla Griffith

Job title: Test proctor
Socials: https://linkedin.com/in/carla-griffith-73201225

4.7.1 Carla Griffith’s working period at prometric

4.7.2 Gantt plot of Carla Griffith’s experience

---

4.8 Cengiz Ozkaynak

Job title: Interim assistent controller
Socials: https://linkedin.com/in/cengizozkaynak

4.8.1 Cengiz Ozkaynak’s working period at prometric

4.8.2 Gantt plot of Cengiz Ozkaynak’s experience

---

4.9 Cf-Sp Brad Peters; Cfbe

Job title: Prometric approved proctor
Socials: https://linkedin.com/in/chefbradpeters

4.9.1 Cf-Sp Brad Peters; Cfbe’s working period at prometric

4.9.2 Gantt plot of Cf-Sp Brad Peters; Cfbe’s experience

---

4.10 Chad Dyar

Job title: Director of sales enablement
Socials: https://linkedin.com/in/chad-t-dyar-0ba6324 | https://twitter.com/chadtd1 | https://klout.com/chadtd1 | https://facebook.com/chadtd1 | https://linkedin.com/in/chadtdyar

4.10.1 Chad Dyar’s working period at prometric

4.10.2 Gantt plot of Chad Dyar’s experience

---

4.11 Christine Hayek

Job title: Director of client success
Socials: https://foursquare.com/user/3024301 | https://twitter.com/chchayek | https://pinterest.com/chchayek202 | https://linkedin.com/in/christine-hayek-b6189b22 | https://facebook.com/christine.hayek.7

4.11.1 Christine Hayek’s working period at prometric

4.11.2 Gantt plot of Christine Hayek’s experience

---

4.12 D.B.A Charice Hayes

Job title: Subject matter expert consultant
Socials: https://twitter.com/all4onetutoring | https://linkedin.com/in/charicehayes

4.12.1 D.B.A Charice Hayes’s working period at prometric

4.12.2 Gantt plot of D.B.A Charice Hayes’s experience

---

4.13 Dallas Coffman

Job title: Subject matter expert and tax specialist for irs special enrollment examination
Socials: https://linkedin.com/in/dallascoffman

4.13.1 Dallas Coffman’s working period at prometric

4.13.2 Gantt plot of Dallas Coffman’s experience

---

4.14 Ed Mooers

Job title: Senior manager, software development
Socials: https://linkedin.com/in/emooers

4.14.1 Ed Mooers’s working period at prometric

4.14.2 Gantt plot of Ed Mooers’s experience

---

4.15 Farvardin Jahansooz

Job title: Senior application engineer
Socials: https://linkedin.com/in/--357134 | https://linkedin.com/in/dean-farvardin-jahansooz-357134 | https://linkedin.com/in/farvardin-jahansooz-357134

4.15.1 Farvardin Jahansooz’s working period at prometric

4.15.2 Gantt plot of Farvardin Jahansooz’s experience

---

4.16 Felicia Boamah

Job title: Test center administrator
Socials: https://facebook.com/divapimponess | https://linkedin.com/in/felicia-boamah-429b7943 | https://linkedin.com/in/feliciaboamah

4.16.1 Felicia Boamah’s working period at prometric

4.16.2 Gantt plot of Felicia Boamah’s experience

---

4.17 Hanneke Van Der Helm

Job title: Senior human resources business partner global
Socials: https://linkedin.com/in/hannekevanderhelm

4.17.1 Hanneke Van Der Helm’s working period at prometric

4.17.2 Gantt plot of Hanneke Van Der Helm’s experience

---

4.18 Janine Allen

Job title: Consultant
Socials: https://linkedin.com/in/drjaninecmallen | https://twitter.com/drjaninecmallen

4.18.1 Janine Allen’s working period at prometric

4.18.2 Gantt plot of Janine Allen’s experience

---

4.19 Kiana Dancil

Job title: Client success manager
Socials: https://linkedin.com/in/kiana-dancil-7aaa6810 | https://linkedin.com/in/kiana-dancil-ccsp-7aaa6810

4.19.1 Kiana Dancil’s working period at prometric

4.19.2 Gantt plot of Kiana Dancil’s experience

---

4.20 Lyn Parker

Job title: Subject matter expert and item writer for commonwealth of massachusetts division of insurance
Socials: https://linkedin.com/in/lynparker1

4.20.1 Lyn Parker’s working period at prometric

4.20.2 Gantt plot of Lyn Parker’s experience

---

4.21 Marc Solis

Job title: Test center administrator
Socials: https://facebook.com/floydthepink | https://linkedin.com/in/marcodavidsolis | https://youtube.com/user/ucnerkhxr5nz4irogtckrb7w

4.21.1 Marc Solis’s working period at prometric

4.21.2 Gantt plot of Marc Solis’s experience

---

4.22 Martin Peen

Job title: Business partner
Socials: https://linkedin.com/in/martin-peen-077a506 | https://facebook.com/martin.peen

4.22.1 Martin Peen’s working period at prometric

4.22.2 Gantt plot of Martin Peen’s experience

---

4.23 Michael Irwin

Job title: Senior database engineer
Socials: https://linkedin.com/in/dcpking

4.23.1 Michael Irwin’s working period at prometric

4.23.2 Gantt plot of Michael Irwin’s experience

---

4.24 Michael Re

Job title: Consultant
Socials: https://linkedin.com/in/fabriprojects

4.24.1 Michael Re’s working period at prometric

4.24.2 Gantt plot of Michael Re’s experience

---

4.25 Mike Calvo

Job title: Consultant
Socials: https://linkedin.com/in/mike-calvo-002998 | https://klout.com/mike_calvo | https://twitter.com/mike_calvo | https://github.com/mikecalvo | https://youtube.com/user/ucvhrmbazmfmt9f1d_awclvw | https://stackoverflow.com/users/2440734

4.25.1 Mike Calvo’s working period at prometric

4.25.2 Gantt plot of Mike Calvo’s experience

---

4.26 Patricia Fullagar

Job title: Consulltant project manager
Socials: https://facebook.com/pat.fullagar | https://linkedin.com/in/patricia-fullagar-3641414 | https://linkedin.com/in/patricia-fullagar-600b233 | https://linkedin.com/in/patricia-fullagar-pmp-1954571

4.26.1 Patricia Fullagar’s working period at prometric

4.26.2 Gantt plot of Patricia Fullagar’s experience

---

4.27 Patrick Maher

Job title: Account manager
Socials: https://facebook.com/pat.maher | https://linkedin.com/in/patrick-maher-ba3a9171

4.27.1 Patrick Maher’s working period at prometric

4.27.2 Gantt plot of Patrick Maher’s experience

---

4.28 S Christine Cavataio

Job title: Subject matter expert, interior design
Socials: https://linkedin.com/in/chris-cavataio-34865880 | https://linkedin.com/in/s-christine-cavataio-34865880

4.28.1 S Christine Cavataio’s working period at prometric

4.28.2 Gantt plot of S Christine Cavataio’s experience

---

4.29 Sharon Fleming

Job title: Test center administer
Socials: https://meetup.com/members/12561409 | https://linkedin.com/in/managersharonfleming | https://indeed.com/r/sharon-fleming | https://linkedin.com/in/sharon-fleming-847212a | https://facebook.com/sharon.fleming.129 | https://twitter.com/slmfleming

4.29.1 Sharon Fleming’s working period at prometric

4.29.2 Gantt plot of Sharon Fleming’s experience

---

4.30 Stephen Jones

Job title: Technical editor
Socials: https://linkedin.com/in/spjcomm | https://linkedin.com/in/stephen-jones-29669612

4.30.1 Stephen Jones’s working period at prometric

4.30.2 Gantt plot of Stephen Jones’s experience

---

df_full_personas_who_worked_in_company.write_parquet(current_company_parquet)